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Record W2034755731 · doi:10.4161/nucl.1.3.11444

Traffic control at the nuclear pore

2010· article· en· W2034755731 on OpenAlexafffund
Mohamed Kodiha, Noah Crampton, Sanhita Shrivastava, Rehan Umar, Ursula Stochaj

Bibliographic record

VenueNucleus · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsNuclear poreNucleoporinNuclear transportRibonucleoproteinCell biologyBiologyCytoplasmOrganelleNucleusCell nucleusComputational biologyGeneticsRNAGene

Abstract

fetched live from OpenAlex

The proper communication between organelles is essential for many aspects of eukaryotic life. The coordination of nuclear and cytoplasmic activities in particular is of pivotal importance and depends on transport in and out of the nucleus. The material which translocates through nuclear pores is diverse; it includes numerous proteins, RNAs and large ribonucleoprotein complexes like ribosomal subunits. To ensure the correct nucleocytoplasmic distribution of these components, appropriate mechanisms have to be in place which control traffic across the nuclear envelope. A growing number of studies support the notion that transport through nuclear pore complexes is intimately linked to cell physiology. As such, it has become evident that changes in the cellular environment, either by externally applied stress, aging or disease, alter nuclear traffic. Due to the progress made in the past few years, we are now beginning to understand these processes at the molecular level. Thus, the concept emerges that stress or disease conditions correlate with signaling events which aim at the nuclear transport apparatus. Here, we summarize results from recent publications that provide evidence for the hypothesis that changes in cell physiology modulate nuclear traffic by targeting multiple transport factors. We propose that this traffic control is at least in part mediated by specific signaling events.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.188
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2010
Admission routes2
Has abstractyes

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